Minimal Increase in Total Hip Arthroplasty Surgical Procedural Time with the Use of a Novel Surgical Navigation Tool
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Computer-assisted navigation has proven effective at improving the accuracy of component placement during Total Hip Arthroplasty (THA); however, the material costs, line-of-site issues and potential for significant time increases have limited their widespread use. Objective: The purpose of this study was to investigate the impact of an imageless navigation device on surgical time, when compared with standard mechanical guides. Methods: We retrospectively reviewed prospectively collected data from 61 consecutive primary unilateral THA cases (posterior approach) performed by a single surgeon. Procedural time (incision to closure) for THA performed with (intervention) or without (control) a computer-assisted navigation system was compared. In the intervention group, the additional time associated with the use of the device was recorded. Mean times were compared using independent samples t-tests with statistical significance set a priori at p<0.05. Results: There was no statistically significant difference between procedural time in the intervention and control groups (102.3±28.3 mins vs. 99.1±14.7 mins, p=0.60). The installation and use of the navigation device accounted for an average of 2.9 mins (SD: 1.6) per procedure, of which device-related setup performed prior to skin incision accounted for 1.1 mins (SD: 1.1) and intra-operative tasks accounted for 1.6 mins (SD: 1.2). Conclusion: In this series of 61 consecutive THAs performed by a single surgeon, the set-up and hands-on utilization of a novel surgical navigation tool required an additional 2.9 minutes per case. We suggest that the intraoperative benefits of this novel computer-assisted navigation platform outweigh the minimal operative time spent using this technology.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it